A financial recommender system couples two objectives: ranking for preference alignment so that users actually adopt the recommendations, and ranking for outcome quality so that adoption translates into value. These objectives can conflict: return-driven lists may narrow diversification and miss user tastes, while relevance-only lists deliver weak realized returns. To address these problems, we propose Risk-aware Utility re-RAnking (RURA), a plug-in method that operates on the upstream top candidates and optimizes a user-specific expected-utility objective. RURA injects investor risk tolerance into the utility, includes a likelihood-aware variant that integrates calibrated adoption probabilities, and uses a single hyperparameter to control diversification to preserve upstream order while trading minimal nDCG loss for ROI gains. Experiments on a real-world dataset demonstrate that RURA outperforms risk-aware baselines in ROI while keeping nDCG within the range of a strong risk-aware baseline and delivering higher expected utility across risk groups.

This study aims to evaluate the sentiment of financial texts using large language models (LLMs) and to empirically determine whether LLMs exhibit company-specific biases in sentiment analysis. Specifically, we examine the impact of general knowledge about firms on the sentiment measurement of texts by LLMs. Firstly, we compare the sentiment scores of financial texts by LLMs when the company name is explicitly included in the prompt versus when it is not. We define and quantify companyspecific bias as the difference between these scores. Next, we construct an economic model to theoretically evaluate the impact of sentiment bias on investor behavior. This model helps us understand how biased LLM investments, when widespread, can distort stock prices. This implies the potential impact on stock prices if investments driven by biased LLMs become dominant in the future. Finally, we conduct an empirical analysis using Japanese financial text data to examine the relationship between firm-specific sentiment bias, corporate characteristics, and stock performance.

Machine learning is an increasingly popular tool with some success in predicting stock prices. One promising method is the Trader-Company (TC) method, which takes into account the dynamism of the stock market and has both high predictive power and interpretability. Machine learning-based stock prediction methods, including the TC method, have been concentrating on point prediction. However, point prediction in the absence of uncertainty estimates lacks credibility quantification and raises concerns about safety. The challenge in this paper is to make an investment strategy that combines high predictive power and the ability to quantify uncertainty. We propose a novel approach called Uncertainty Aware Trader-Company Method (UTC) method. The core idea of this approach is to combine the strengths of both frameworks by merging the TC method with the probabilistic modeling, which provides probabilistic predictions and uncertainty estimations. We expect this to retain the predictive power and interpretability of the TC method while capturing the uncertainty. We theoretically prove that the proposed method estimates the posterior variance and does not introduce additional biases from the original TC method. We conduct a comprehensive evaluation of our approach based on the synthetic and real market datasets. We confirm with synthetic data that the UTC method can detect situations where the uncertainty increases and the prediction is difficult. We also confirmed that the UTC method could detect abrupt changes in data-generating distributions. We demonstrate with real market data that the UTC method can achieve higher returns and lower risks than baselines.

In this paper, we focus on the generation of time-series data using neural networks. It is often the case that input time-series data have only one realized (and usually irregularly sampled) path, which makes it difficult to extract time-series characteristics, and its noise structure is more complicated than i.i.d. type. Time series data, especially from hydrology, telecommunications, economics, and finance, exhibit long-term memory also called long-range dependency (LRD). The main purpose of this paper is to artificially generate time series with the help of neural networks, making the LRD of paths into account. We propose fSDE-Net: neural fractional Stochastic Differential Equation Network. It generalizes the neural stochastic differential equation model by using fractional Brownian motion with a Hurst index larger than half, which exhibits the LRD property. We derive the solver of fSDE-Net and theoretically analyze the existence and uniqueness of the solution to fSDE-Net. Our experiments with artificial and real time-series data demonstrate that the fSDE-Net model can replicate distributional properties well.

Stock return predictability is an important research theme as it reflects our economic and social organization, and significant efforts are made to explain the dynamism therein. Statistics of strong explanative power, called "factor", have been proposed to summarize the essence of predictive stock returns. The challenge here is to make a multi-factor investment strategy that is consistent over a reasonably long period based on supervised machine learning. Although machine learning methods are increasingly popular in stock return prediction, an inference of the stock return is highly elusive, and naive use of complex machine learning methods easily overfits the current data and results in poor performance on future data. We propose a principled stock return prediction framework that we call Ranked Information Coefficient Neural Network (RIC-NN) that alleviates the overfitting. RIC-NN addresses the difficulty that arises in nonconvex machine learning: Namely, initialization and the stopping of the training model and the transfer among several different tasks (markets). RIC-NN is a deep learning approach and includes the following three novel ideas: (1) nonlinear multi-factor approach, (2) stopping criteria with ranked information coefficient (rank IC), and (3) deep transfer learning among multiple regions. Experimental comparison with the stocks in the Morgan Stanley Capital International indices shows that RIC-NN outperforms not only off-the-shelf machine learning methods but also the average return of major equity investment funds in the last fourteen years.